Gaggimate Profiles

Gaggimate Profile Creation Skill

Create custom espresso extraction profiles for Gaggimate-equipped machines. Gaggimate supports Simple and Pro profile types, with Pro profiles offering pressure profiling, flow control, and complex transitions.

Enriched with patterns from gaggimate-barista by Charlie Hall.

Workflow

Step 1: Gather Information

If not provided, load the user's equipment, basket size, and preferences:

Use: manage_user_setup(action="read")

Ask about anything still missing:

• Coffee type/origin and roast level
• Processing method (washed, natural, honey, anaerobic — affects target pressure)
• Dose amount (dose = basket size; don't underdose)
• Desired output ratio (1:2 is classic)
• Flavor goals (more body, more acidity, reduce bitterness, etc.)
• Whether they have a Bluetooth scale (for volumetric stop conditions)

Step 2: Select Profile Pattern

Load the relevant knowledge files via MCP tools to determine settings:

• Temperature: read_knowledge(action="read", filename="ESPRESSO_BREWING_BASICS") → "Temperature Guidelines by Roast"
• Pressure: read_knowledge(action="read", filename="PRESSURE_GUIDE") → roast × processing matrix
• Profile pattern: read_knowledge(action="read", filename="PROFILE_LIBRARY") → select by roast, process, and style

Quick reference for common patterns:

Step 3: Load Reference Files (conditionally)

Default: load ZERO reference files. Steps 2 + 4 (knowledge files + inline JSON template) are sufficient for standard profiles built from library patterns. Only load a reference file when a specific trigger applies.

Stop rule: Load at most 2 reference files per profile creation session.

Step 4: Generate Profile JSON

Always output complete, valid JSON with ALL required fields:

{
  "label": "Profile Name",
  "type": "pro",
  "description": "Optional description",
  "temperature": 93,
  "phases": [
    {
      "name": "Phase Name",
      "phase": "preinfusion|brew|decline",
      "valve": 1,
      "duration": 25,
      "temperature": 0,
      "transition": { "type": "instant", "duration": 0, "adaptive": true },
      "pump": { "target": "pressure", "pressure": 9, "flow": 0 },
      "targets": [{ "type": "volumetric", "operator": "gte", "value": 36 }]
    }
  ]
}

Phase naming for shot analysis: The analyze_shot tool classifies phases by substring-matching the name field. Use names containing recognised keywords so specific diagnostic metrics are computed per phase:

• Preinfusion phases: include preinfusion, pre-infusion, soak, bloom, or fill (e.g. "Pre-infusion", "Bloom", "Long Soak")
• Decline/taper phases: include decline, taper, or cooldown (e.g. "Decline", "Gentle Taper")
• Brew phases: any other name works (e.g. "Ramp", "Hold", "Extraction")

If the name contains none of these keywords, the analyzer falls back to telemetry heuristics — but explicit naming is always preferred.

Volumetric target: Always set to dose × ratio using the user's basket size. Library profiles in PROFILE_LIBRARY.md are sized for 22g — adjust if user's basket differs.

Step 5: Explain the Profile

After generating JSON, explain:

• What each phase does and why
• How the profile addresses the user's flavor goals
• Any adjustments they might want to try

Step 6: Upload to Device

After generating the profile, confirm with user then upload:

1. Ask for confirmation: "Profile ready. Shall I upload it to your machine?"
2. Create or update via manage_profile MCP tool (action: create for new, update for existing)
3. Verify the upload succeeded by checking the MCP response

Always add [AI] suffix to profile names created by the agent.

Quick Profile Patterns

Classic 9-Bar (Medium Roasts)

Pre-infusion (4s, flow 3 ml/s) → Ramp (4s to 9 bar) → Hold (25s at 9 bar) → Stop at weight

Bloom Profile (Light Roasts)

Fill (5s, flow 2.5 ml/s) → Bloom (8s, pump off) → Ramp (5s to 9 bar) → Hold (20s) → Stop at weight

Lever Simulation (Any Roast)

Pre-infusion (5s, flow 3 ml/s) → Peak (5s to 9 bar) → Linear decline (25s to 3 bar) → Stop at weight

Dark Roast Low Pressure

Pre-infusion (4s, flow 2.5 ml/s) → Ramp (3s to 7 bar) → Hold (25s) → Taper (4s to 4 bar)

Essential Pump Modes

// Pressure mode - maintain specific pressure
{ "target": "pressure", "pressure": 9, "flow": 4 }

// Flow mode - maintain specific flow rate
{ "target": "flow", "pressure": 9, "flow": 2.5 }

// Adaptive flow - auto-adjust to puck resistance
{ "target": "flow", "pressure": 9, "flow": -1 }

// Power mode (for bloom - pump off)
{ "target": "power", "pressure": 0, "flow": 0 }

Essential Transition Types

Output Requirements

1. Always output complete, valid JSON that can be directly imported
2. Include all required fields - don't omit any phase properties
3. Use sensible defaults - valve: 1, adaptive: true for most cases
4. Add a volumetric target on the final extraction phase (if scale available)
5. Explain your choices after the JSON
6. Upload to device via MCP after user confirms

Resources

• Documentation: https://docs.gaggimate.eu/
• Discord Community: https://discord.gg/APw7rgPGPf
• Web Interface: http://gaggimate.local/profiles
• GitHub: https://github.com/jniebuhr/gaggimate

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